The OMA Orthologs Matrix Project

C Dessimoz, GM Cannarozzi, M Gil, D Margadant, A Roth, A Schneider and GH Gonnet, 2005:
OMA, A Comprehensive, Automated Project for the Identification of Orthologs from Complete Genome Data: Introduction and First Achievements,
LNCS 3678: Comparative Genomics: RECOMB 2005 International Workshop, RCG 2005, Dublin, Ireland.
SpringerLink

A Schneider, C Dessimoz and GH Gonnet, 2007:
OMA Browser — Exploring Orthologous Relations across 352 Complete Genomes
Bioinformatics 23(16): 2180-2182.
Open Access

A Roth, GH Gonnet and C Dessimoz, 2008:
Algorithm of OMA for Large-Scale Orthology Inference,
BMC Bioinformatics, 2008, 9:518
BMC

AM Altenhoff and C Dessimoz, 2009:
Phylogenetic and Functional Assessment of Orthologs Inference Projects and Methods,
PLoS Comput Biol 5(1): e1000262
PLoS

AM Altenhoff, A Schneider, GH Gonnet and C Dessimoz, 2011:
OMA 2011: orthology inference among 1000 complete genomes,
Nucl. Acids Res., 2011, 39(suppl 1): D289-D294
NAR

The orthologs matrix project (OMA) is based on a massive cross-comparison of complete genomes that is currently performed by the CBRG group. This allows us to identify orthologous genes.

The orthologs matrix project started in summer 2004 and since then over 300 CPUs are performing the enormous task of comparing whole genomes.

Ressources

• The OMA Browser offers search options to find sequences and groups. Orthology predictions can be downloaded for further analysis.
• An brief description of the OMA algorithm.
• A status page providing the latest trees and statistics about the OMA project.
• Species trees for different subsets of organisms of all three domains of life, downloadable in various formats.

The following diagram provide an overview of data flow:

The various applications for this project include

Building phylogenetic trees of species. The large amount of data (i.e. whole genomes) allows the construction of trees based on an unprecendented amount of information using several methods like parsimony, amino acid (PAM) distance, codon distance and several others.

Finding protein families based on co-occurence of orthologous genes in different genomes.

Identifying cases of lateral gene transfer.